Investigating the translation capabilities of Large Language Models trained on parallel data only

• URL: http://arxiv.org/abs/2406.09140v1
• Date: Thu, 13 Jun 2024 14:08:56 GMT
• Title: Investigating the translation capabilities of Large Language Models trained on parallel data only
• Authors: Javier García Gilabert, Carlos Escolano, Aleix Sant Savall, Francesca De Luca Fornaciari, Audrey Mash, Xixian Liao, Maite Melero,
• Abstract summary: Large Language Models (LLMs) have demonstrated exceptional proficiency across a broad spectrum of Natural Language Processing (NLP) tasks. We introduce PLUME, a collection of three 2B LLMs featuring varying vocabulary sizes (32k, 128k, and 256k) trained exclusively on Catalan-centric parallel examples. These models perform comparably to previous encoder-decoder architectures on 16 supervised translation directions and 56 zero-shot ones.
• Score: 1.5974665548135587
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, Large Language Models (LLMs) have demonstrated exceptional proficiency across a broad spectrum of Natural Language Processing (NLP) tasks, including Machine Translation. However, previous methods predominantly relied on iterative processes such as instruction fine-tuning or continual pre-training, leaving unexplored the challenges of training LLMs solely on parallel data. In this work, we introduce PLUME (Parallel Language Model), a collection of three 2B LLMs featuring varying vocabulary sizes (32k, 128k, and 256k) trained exclusively on Catalan-centric parallel examples. These models perform comparably to previous encoder-decoder architectures on 16 supervised translation directions and 56 zero-shot ones. Utilizing this set of models, we conduct a thorough investigation into the translation capabilities of LLMs, probing their performance, the impact of the different elements of the prompt, and their cross-lingual representation space.

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